AIMC Topic: Wakefulness

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Directed Evolution of a Selective and Sensitive Serotonin Sensor via Machine Learning.

Cell Dec 16, 2020
Serotonin plays a central role in cognition and is the target of most pharmaceuticals for psychiatric disorders. Existing drugs have limited efficacy; creation of improved versions will require better understanding of serotonergic circuitry, which ha...
Mice Mice, Inbred C57BL Kinetics Amino Acid Sequence Wakefulness Machine Learning Algorithms Brain Animals Protein Binding Directed Molecular Evolution Serotonin Photons Amygdala Serotonin Plasma Membrane Transport Proteins Sleep Linear Models HEK293 Cells Behavior, Animal Binding Sites Humans
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A comparison of regularized logistic regression and random forest machine learning models for daytime diagnosis of obstructive sleep apnea.

Medical & biological engineering & computing Aug 17, 2020
A major challenge in big and high-dimensional data analysis is related to the classification and prediction of the variables of interest by characterizing the relationships between the characteristic factors and predictors. This study aims to assess ...
Middle Aged Female Diagnosis, Computer-Assisted Aged Sleep Apnea, Obstructive Adolescent Humans Young Adult Logistic Models Wakefulness Machine Learning Adult Signal Processing, Computer-Assisted Databases, Factual Breath Tests Male Trachea
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Neurotransmitter networks in mouse prefrontal cortex are reconfigured by isoflurane anesthesia.

Journal of neurophysiology Apr 29, 2020
This study quantified eight small-molecule neurotransmitters collected simultaneously from prefrontal cortex of C57BL/6J mice ( = 23) during wakefulness and during isoflurane anesthesia (1.3%). Using isoflurane anesthesia as an independent variable e...
Machine Learning Animals Male Prefrontal Cortex Mice Acetylcholine Nerve Net Anesthetics, Inhalation Adenosine Microdialysis Mice, Inbred C57BL Norepinephrine Isoflurane Wakefulness Anesthesia Unconsciousness
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Constructing a Consciousness Meter Based on the Combination of Non-Linear Measurements and Genetic Algorithm-Based Support Vector Machine.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Jan 8, 2020
OBJECTIVE: Constructing a framework to evaluate consciousness is an important issue in neuroscience research and clinical practice. However, there is still no systematic framework for quantifying altered consciousness along the dimensions of both lev...
Genetics Consciousness Disorders Consciousness Monitors Coma Male Middle Aged Support Vector Machine Anesthesia, General Electroencephalography Healthy Volunteers Wakefulness Nonlinear Dynamics Consciousness Entropy Humans Algorithms Female Reproducibility of Results
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Pharmacologically informed machine learning approach for identifying pathological states of unconsciousness via resting-state fMRI.

NeuroImage Oct 29, 2019
Determining the level of consciousness in patients with disorders of consciousness (DOC) remains challenging. To address this challenge, resting-state fMRI (rs-fMRI) has been widely used for detecting the local, regional, and network activity differe...
Deep Sedation Unconsciousness Persistent Vegetative State Consciousness Disorders Conscious Sedation Male Support Vector Machine Brain Humans Middle Aged Magnetic Resonance Imaging Female Neural Networks, Computer Functional Neuroimaging Anesthesia, General Aged Machine Learning Adolescent Wakefulness Young Adult Child Rest Adult
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Novel drug-independent sedation level estimation based on machine learning of quantitative frontal electroencephalogram features in healthy volunteers.

British journal of anaesthesia Jul 18, 2019
BACKGROUND: Sedation indicators based on a single quantitative EEG (QEEG) feature have been criticised for their limited performance. We hypothesised that integration of multiple QEEG features into a single sedation-level estimator using a machine le...
Humans Machine Learning Reproducibility of Results Electroencephalography Reference Values Wakefulness Anesthetics Frontal Lobe Consciousness Monitors
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SPINDLE: End-to-end learning from EEG/EMG to extrapolate animal sleep scoring across experimental settings, labs and species.

PLoS computational biology Apr 18, 2019
Understanding sleep and its perturbation by environment, mutation, or medication remains a central problem in biomedical research. Its examination in animal models rests on brain state analysis via classification of electroencephalographic (EEG) sign...
Sleep Models, Animal Rats Humans Electroencephalography Neural Networks, Computer Signal Processing, Computer-Assisted Machine Learning Electromyography Animals Wakefulness Mice Computational Biology
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Deep learning in the cross-time frequency domain for sleep staging from a single-lead electrocardiogram.

Physiological measurement Dec 21, 2018
OBJECTIVE: This study classifies sleep stages from a single lead electrocardiogram (ECG) using beat detection, cardiorespiratory coupling in the time-frequency domain and a deep convolutional neural network (CNN).
Humans Neural Networks, Computer Signal Processing, Computer-Assisted Deep Learning Wakefulness Time Factors Sleep Stages Electrocardiography Support Vector Machine
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Adapting artificial neural networks to a specific driver enhances detection and prediction of drowsiness.

Accident; analysis and prevention Sep 20, 2018
Monitoring car drivers for drowsiness is crucial but challenging. The high inter-individual variability observed in measurements raises questions about the accuracy of the drowsiness detection process. In this study, we sought to enhance the performa...
Computer Systems Automobile Driving Sleepiness Wakefulness Humans Machine Learning Neural Networks, Computer Computer Simulation Female Male
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Multimodal Ambulatory Sleep Detection Using LSTM Recurrent Neural Networks.

IEEE journal of biomedical and health informatics Aug 29, 2018
Unobtrusive and accurate ambulatory methods are needed to monitor long-term sleep patterns for improving health. Previously developed ambulatory sleep detection methods rely either in whole or in part on self-reported diary data as ground truth, whic...
Smartphone Signal Processing, Computer-Assisted Young Adult Skin Temperature Humans Adult Wakefulness Actigraphy Neural Networks, Computer Male Sleep Galvanic Skin Response Algorithms Adolescent Monitoring, Ambulatory Female Wearable Electronic Devices
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